Test Optimal Combination
Kevin Lu
December 18, 2017
1. Source Search Cointegration Combinations
source("./scripts/04-search-cointegration.R", echo = FALSE, print.eval = FALSE)## Parsed with column specification:
## cols(
## date_unix = col_double(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer(),
## source = col_character()
## )
## Parsed with column specification:
## cols(
## date_unix = col_double(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer(),
## source = col_character()
## )
## Parsed with column specification:
## cols(
## date_unix = col_double(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer(),
## source = col_character()
## )
## Parsed with column specification:
## cols(
## date_unix = col_double(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer(),
## source = col_character()
## )
## Parsed with column specification:
## cols(
## date_unix = col_double(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer(),
## source = col_character()
## )
## Parsed with column specification:
## cols(
## date_unix = col_double(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer(),
## source = col_character()
## )## [1] "1: Testing for time resolution 900 from 2017-07-07 to 2017-08-23."
## [1] "2: Testing for time resolution 900 from 2017-01-29 to 2017-03-06."
## [1] "3: Testing for time resolution 7200 from 2017-08-10 to 2017-09-27."
## [1] "4: Testing for time resolution 300 from 2017-03-16 to 2017-04-11."
## [1] "5: Testing for time resolution 900 from 2017-06-03 to 2017-06-18."
## [1] "6: Testing for time resolution 900 from 2017-04-17 to 2017-06-01."
## [1] "7: Testing for time resolution 7200 from 2017-08-19 to 2017-10-03."
## [1] "8: Testing for time resolution 14400 from 2017-02-27 to 2017-03-12."
## [1] "9: Testing for time resolution 300 from 2017-05-12 to 2017-06-04."
## [1] "10: Testing for time resolution 86400 from 2017-02-08 to 2017-03-28."
## # A tibble: 10 x 14
## df_stat_mean crit_value_1pct_mean crit_value_5pct_mean
## <dbl> <dbl> <dbl>
## 1 -3.051071 -3.43 -2.86
## 2 -2.442145 -3.43 -2.86
## 3 -2.413120 -3.43 -2.86
## 4 -3.326170 -3.43 -2.86
## 5 -1.997779 -3.43 -2.86
## 6 -3.263747 -3.43 -2.86
## 7 -2.594638 -3.43 -2.86
## 8 -2.237029 -3.51 -2.89
## 9 -3.767018 -3.43 -2.86
## 10 -2.327580 -3.58 -2.93
## # ... with 11 more variables: crit_value_10pct_mean <dbl>,
## # half_life_mean <dbl>, df_stat_median <dbl>,
## # crit_value_1pct_median <dbl>, crit_value_5pct_median <dbl>,
## # crit_value_10pct_median <dbl>, half_life_median <dbl>,
## # time_resolution <dbl>, start_date <date>, end_date <date>,
## # length <time>## Parsed with column specification:
## cols(
## df_stat = col_double(),
## crit_value_1pct = col_double(),
## crit_value_5pct = col_double(),
## crit_value_10pct = col_double(),
## half_life = col_double(),
## time_resolution = col_integer(),
## start_date = col_date(format = ""),
## end_date = col_date(format = ""),
## length = col_integer()
## )## # A tibble: 18,600 x 9
## df_stat crit_value_1pct crit_value_5pct crit_value_10pct half_life
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -3.012175 -3.43 -2.86 -2.57 384.672689
## 2 -3.162462 -3.43 -2.86 -2.57 560.900234
## 3 -2.598657 -3.43 -2.86 -2.57 44.754316
## 4 -3.835017 -3.43 -2.86 -2.57 1004.935943
## 5 -2.582041 -3.43 -2.86 -2.57 223.135158
## 6 -2.476513 -3.43 -2.86 -2.57 827.426087
## 7 -2.656168 -3.43 -2.86 -2.57 27.449263
## 8 -1.769557 -3.44 -2.87 -2.57 8.631061
## 9 -3.428251 -3.43 -2.86 -2.57 789.819443
## 10 -1.854896 -3.46 -2.88 -2.57 44.109767
## # ... with 18,590 more rows, and 4 more variables: time_resolution <int>,
## # start_date <date>, end_date <date>, length <int>## Parsed with column specification:
## cols(
## df_stat_mean = col_double(),
## crit_value_1pct_mean = col_double(),
## crit_value_5pct_mean = col_double(),
## crit_value_10pct_mean = col_double(),
## half_life_mean = col_double(),
## df_stat_median = col_double(),
## crit_value_1pct_median = col_double(),
## crit_value_5pct_median = col_double(),
## crit_value_10pct_median = col_double(),
## half_life_median = col_double(),
## time_resolution = col_integer(),
## start_date = col_date(format = ""),
## end_date = col_date(format = ""),
## length = col_integer()
## )## # A tibble: 18,600 x 14
## df_stat_mean crit_value_1pct_mean crit_value_5pct_mean
## <dbl> <dbl> <dbl>
## 1 -2.705763 -3.43 -2.86
## 2 -2.205763 -3.43 -2.86
## 3 -2.522635 -3.43 -2.86
## 4 -3.358555 -3.43 -2.86
## 5 -1.919454 -3.43 -2.86
## 6 -2.964059 -3.43 -2.86
## 7 -2.594580 -3.43 -2.86
## 8 -2.211002 -3.51 -2.89
## 9 -3.661327 -3.43 -2.86
## 10 -2.529785 -3.58 -2.93
## # ... with 18,590 more rows, and 11 more variables:
## # crit_value_10pct_mean <dbl>, half_life_mean <dbl>,
## # df_stat_median <dbl>, crit_value_1pct_median <dbl>,
## # crit_value_5pct_median <dbl>, crit_value_10pct_median <dbl>,
## # half_life_median <dbl>, time_resolution <int>, start_date <date>,
## # end_date <date>, length <int>2. Prepare Data and Calculate Statistics
For each time resolution, prepare the pricing data and test for cointegration for all 98 coin pairs. The date of study is 2017-09-01 to 2017-09-30. This period has exhibited strong mean reversion.
pricing_data_300 <- prepare_data(time_resolution = 300, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_900 <- prepare_data(time_resolution = 900, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_1800 <- prepare_data(time_resolution = 1800, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_7200 <- prepare_data(time_resolution = 7200, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_14400 <- prepare_data(time_resolution = 14400, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_86400 <- prepare_data(time_resolution = 86400, start_date = "2017-09-01", end_date = "2017-09-30")
coin_pairs_300 <- calculate_statistics(pricing_data = pricing_data_300, coin_pairs = create_coins())
coin_pairs_900 <- calculate_statistics(pricing_data = pricing_data_900, coin_pairs = create_coins())
coin_pairs_1800 <- calculate_statistics(pricing_data = pricing_data_1800, coin_pairs = create_coins())
coin_pairs_7200 <- calculate_statistics(pricing_data = pricing_data_7200, coin_pairs = create_coins())
coin_pairs_14400 <- calculate_statistics(pricing_data = pricing_data_14400, coin_pairs = create_coins())
coin_pairs_86400 <- calculate_statistics(pricing_data = pricing_data_86400, coin_pairs = create_coins())3. Plot Time Resolution 300
For each time resolution, plot the top 10 coins ranked by the ADF test statistic.
for (i in 1:10) {
coin_y <- coin_pairs_300[["coin_y"]][i]
coin_x <- coin_pairs_300[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[[coin_y]],
coin_x = pricing_data_300[[coin_x]])
}## [1] "Generating plots for BTC_FCT and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00056360 -0.00011963 -0.00000535 0.00010828 0.00095778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00043456 0.00001868 -23.26 <0.0000000000000002 ***
## coin_x 0.37427940 0.00124815 299.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001921 on 8351 degrees of freedom
## Multiple R-squared: 0.915, Adjusted R-squared: 0.915
## F-statistic: 8.992e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00227102 -0.00032479 -0.00002686 0.00030335 0.00162803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00232629 0.00004218 55.15 <0.0000000000000002 ***
## coin_x 2.44475292 0.00815280 299.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0004909 on 8351 degrees of freedom
## Multiple R-squared: 0.915, Adjusted R-squared: 0.915
## F-statistic: 8.992e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000073779 -0.0000013072 0.0000001221 0.0000012134 0.0000070039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000364478 0.0000001387 262.7 <0.0000000000000002 ***
## coin_x 0.0047456380 0.0000268107 177.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001614 on 8351 degrees of freedom
## Multiple R-squared: 0.7896, Adjusted R-squared: 0.7895
## F-statistic: 3.133e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001342 0.000000035 0.000001233 0.000006823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000342914 0.0000001786 192.0 <0.0000000000000002 ***
## coin_x 0.0017825242 0.0000119346 149.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001836 on 8351 degrees of freedom
## Multiple R-squared: 0.7276, Adjusted R-squared: 0.7276
## F-statistic: 2.231e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8470 -0.5730 -0.1290 0.4191 4.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76564090 0.10152549 -96.19 <0.0000000000000002 ***
## coin_x 0.00741490 0.00002486 298.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8201 on 8351 degrees of freedom
## Multiple R-squared: 0.9142, Adjusted R-squared: 0.9142
## F-statistic: 8.894e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_FCT and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00124496 -0.00026763 0.00002131 0.00022386 0.00115275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00498391 0.00005725 -87.06 <0.0000000000000002 ***
## coin_x 166.37415064 0.93993822 177.00 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0003022 on 8351 degrees of freedom
## Multiple R-squared: 0.7896, Adjusted R-squared: 0.7895
## F-statistic: 3.133e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -535.99 -52.48 12.85 73.27 231.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1553.1077 8.5097 182.5 <0.0000000000000002 ***
## coin_x 123.2873 0.4134 298.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105.7 on 8351 degrees of freedom
## Multiple R-squared: 0.9142, Adjusted R-squared: 0.9142
## F-statistic: 8.894e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.7857 -1.6548 -0.3121 1.0235 24.4679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.96177 0.23045 156.1 <0.0000000000000002 ***
## coin_x 1.13133 0.00371 304.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.976 on 8351 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9176
## F-statistic: 9.297e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.4296 -1.3426 -0.0774 1.4087 10.5354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.14007 0.28163 -85.72 <0.0000000000000002 ***
## coin_x 0.81106 0.00266 304.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.366 on 8351 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9176
## F-statistic: 9.297e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00261762 -0.00071480 -0.00007712 0.00068928 0.00282866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0099463 0.0001665 -59.75 <0.0000000000000002 ***
## coin_x 408.1931633 2.7329907 149.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008788 on 8351 degrees of freedom
## Multiple R-squared: 0.7276, Adjusted R-squared: 0.7276
## F-statistic: 2.231e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
4. Plot Time Resolution 900
for (i in 1:10) {
coin_y <- coin_pairs_900[["coin_y"]][i]
coin_x <- coin_pairs_900[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[[coin_y]],
coin_x = pricing_data_900[[coin_x]])
}## [1] "Generating plots for BTC_FCT and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00055808 -0.00012162 -0.00000462 0.00010660 0.00095809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0004369 0.0000323 -13.53 <0.0000000000000002 ***
## coin_x 0.3744102 0.0021583 173.47 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001919 on 2783 degrees of freedom
## Multiple R-squared: 0.9153, Adjusted R-squared: 0.9153
## F-statistic: 3.009e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000072207 -0.0000013040 0.0000001089 0.0000012053 0.0000058784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000364330 0.0000002396 152.1 <0.0000000000000002 ***
## coin_x 0.0047490978 0.0000463101 102.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001611 on 2783 degrees of freedom
## Multiple R-squared: 0.7907, Adjusted R-squared: 0.7907
## F-statistic: 1.052e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00227198 -0.00032548 -0.00002378 0.00030445 0.00161311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00232712 0.00007291 31.92 <0.0000000000000002 ***
## coin_x 2.44477302 0.01409314 173.47 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0004902 on 2783 degrees of freedom
## Multiple R-squared: 0.9153, Adjusted R-squared: 0.9153
## F-statistic: 3.009e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001345 0.000000029 0.000001240 0.000006653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000034272 0.000000309 110.92 <0.0000000000000002 ***
## coin_x 0.001783861 0.000020643 86.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001835 on 2783 degrees of freedom
## Multiple R-squared: 0.7285, Adjusted R-squared: 0.7284
## F-statistic: 7467 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_FCT and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00105765 -0.00026822 0.00002412 0.00022376 0.00111410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00499249 0.00009889 -50.49 <0.0000000000000002 ***
## coin_x 166.50403031 1.62363819 102.55 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0003016 on 2783 degrees of freedom
## Multiple R-squared: 0.7907, Adjusted R-squared: 0.7907
## F-statistic: 1.052e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7046 -1.6898 -0.3227 1.0009 24.1214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.967359 0.398511 90.25 <0.0000000000000002 ***
## coin_x 1.131332 0.006417 176.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.971 on 2783 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.9178
## F-statistic: 3.108e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1291 -1.3225 -0.0763 1.4338 8.8500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.167337 0.487184 -49.61 <0.0000000000000002 ***
## coin_x 0.811269 0.004602 176.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.362 on 2783 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.9178
## F-statistic: 3.108e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00239641 -0.00071868 -0.00008315 0.00069136 0.00283113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0099584 0.0002878 -34.60 <0.0000000000000002 ***
## coin_x 408.3809852 4.7259022 86.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000878 on 2783 degrees of freedom
## Multiple R-squared: 0.7285, Adjusted R-squared: 0.7284
## F-statistic: 7467 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7288 -0.5774 -0.1290 0.4189 4.2573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.73062649 0.17668477 -55.07 <0.0000000000000002 ***
## coin_x 0.00740622 0.00004327 171.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 2783 degrees of freedom
## Multiple R-squared: 0.9132, Adjusted R-squared: 0.9132
## F-statistic: 2.929e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -536.07 -53.24 12.89 73.00 215.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1552.7191 14.8285 104.7 <0.0000000000000002 ***
## coin_x 123.3072 0.7204 171.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 106.4 on 2783 degrees of freedom
## Multiple R-squared: 0.9132, Adjusted R-squared: 0.9132
## F-statistic: 2.929e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
5. Plot Time Resolution 1800
for (i in 1:10) {
coin_y <- coin_pairs_1800[["coin_y"]][i]
coin_x <- coin_pairs_1800[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[[coin_y]],
coin_x = pricing_data_1800[[coin_x]])
}## [1] "Generating plots for BTC_FCT and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00054158 -0.00012031 -0.00000488 0.00010781 0.00095790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00043606 0.00004576 -9.528 <0.0000000000000002 ***
## coin_x 0.37436682 0.00305825 122.412 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001924 on 1391 degrees of freedom
## Multiple R-squared: 0.9151, Adjusted R-squared: 0.915
## F-statistic: 1.498e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00227054 -0.00033010 -0.00002274 0.00030218 0.00152761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0023288 0.0001033 22.55 <0.0000000000000002 ***
## coin_x 2.4442798 0.0199676 122.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0004917 on 1391 degrees of freedom
## Multiple R-squared: 0.9151, Adjusted R-squared: 0.915
## F-statistic: 1.498e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000072230 -0.0000013105 0.0000001077 0.0000012007 0.0000042681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000364865 0.0000003386 107.74 <0.0000000000000002 ***
## coin_x 0.0047382118 0.0000654686 72.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001612 on 1391 degrees of freedom
## Multiple R-squared: 0.7902, Adjusted R-squared: 0.79
## F-statistic: 5238 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6872 -1.6639 -0.3182 0.9912 24.1493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.992859 0.560945 64.17 <0.0000000000000002 ***
## coin_x 1.130652 0.009035 125.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.957 on 1391 degrees of freedom
## Multiple R-squared: 0.9184, Adjusted R-squared: 0.9184
## F-statistic: 1.566e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_FCT and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00078675 -0.00026591 0.00002464 0.00021892 0.00111631
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0050081 0.0001403 -35.69 <0.0000000000000002 ***
## coin_x 166.7640343 2.3042050 72.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0003024 on 1391 degrees of freedom
## Multiple R-squared: 0.7902, Adjusted R-squared: 0.79
## F-statistic: 5238 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1857 -1.3133 -0.0869 1.3680 8.8470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.263151 0.686976 -35.32 <0.0000000000000002 ***
## coin_x 0.812293 0.006491 125.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.354 on 1391 degrees of freedom
## Multiple R-squared: 0.9184, Adjusted R-squared: 0.9184
## F-statistic: 1.566e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075557 -0.0000013567 -0.0000000041 0.0000012434 0.0000065578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000343156 0.0000004359 78.73 <0.0000000000000002 ***
## coin_x 0.0017808785 0.0000291278 61.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001833 on 1391 degrees of freedom
## Multiple R-squared: 0.7288, Adjusted R-squared: 0.7286
## F-statistic: 3738 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00046434 -0.00013920 -0.00003941 0.00010537 0.00093452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00318808 0.00004553 70.02 <0.0000000000000002 ***
## coin_x 0.12154940 0.00304290 39.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001915 on 1391 degrees of freedom
## Multiple R-squared: 0.5343, Adjusted R-squared: 0.5339
## F-statistic: 1596 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00040455 -0.00013234 -0.00003214 0.00009584 0.00111191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00329043 0.00003672 89.60 <0.0000000000000002 ***
## coin_x 0.33232514 0.00709972 46.81 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001748 on 1391 degrees of freedom
## Multiple R-squared: 0.6117, Adjusted R-squared: 0.6114
## F-statistic: 2191 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7184 -0.5726 -0.1294 0.4224 4.2610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76197830 0.24983070 -39.07 <0.0000000000000002 ***
## coin_x 0.00741325 0.00006119 121.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 1391 degrees of freedom
## Multiple R-squared: 0.9134, Adjusted R-squared: 0.9134
## F-statistic: 1.468e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
6. Plot Time Resolution 7200
for (i in 1:10) {
coin_y <- coin_pairs_7200[["coin_y"]][i]
coin_x <- coin_pairs_7200[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[[coin_y]],
coin_x = pricing_data_7200[[coin_x]])
}## [1] "Generating plots for BTC_FCT and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00052091 -0.00011245 -0.00000505 0.00010542 0.00077570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00042656 0.00008836 -4.828 0.00000208 ***
## coin_x 0.37359044 0.00590826 63.232 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001862 on 347 degrees of freedom
## Multiple R-squared: 0.9201, Adjusted R-squared: 0.9199
## F-statistic: 3998 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00179524 -0.00033240 -0.00003034 0.00029028 0.00151299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0022373 0.0002013 11.12 <0.0000000000000002 ***
## coin_x 2.4629726 0.0389514 63.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0004782 on 347 degrees of freedom
## Multiple R-squared: 0.9201, Adjusted R-squared: 0.9199
## F-statistic: 3998 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6386 -1.6524 -0.2642 0.9648 19.3184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.99738 1.08940 33.04 <0.0000000000000002 ***
## coin_x 1.12981 0.01756 64.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.855 on 347 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9224
## F-statistic: 4140 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000071754 -0.0000012264 0.0000002238 0.0000012103 0.0000033546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000362056 0.0000006904 52.44 <0.0000000000000002 ***
## coin_x 0.0047877417 0.0001336227 35.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00000164 on 347 degrees of freedom
## Multiple R-squared: 0.7872, Adjusted R-squared: 0.7866
## F-statistic: 1284 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075117 -0.0000013231 -0.0000000155 0.0000012043 0.0000060980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000341619 0.0000008857 38.57 <0.0000000000000002 ***
## coin_x 0.0017887492 0.0000592241 30.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001867 on 347 degrees of freedom
## Multiple R-squared: 0.7244, Adjusted R-squared: 0.7236
## F-statistic: 912.2 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_DCR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0045184 -0.0015447 -0.0001920 0.0009797 0.0105041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.059437 0.001297 45.82 <0.0000000000000002 ***
## coin_x 1.636735 0.162343 10.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002497 on 347 degrees of freedom
## Multiple R-squared: 0.2266, Adjusted R-squared: 0.2243
## F-statistic: 101.6 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.3973 -1.2895 0.0341 1.3039 8.8491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.68618 1.34197 -18.39 <0.0000000000000002 ***
## coin_x 0.81665 0.01269 64.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.278 on 347 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9224
## F-statistic: 4140 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_FCT and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00071354 -0.00026205 0.00001448 0.00020874 0.00109150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0048626 0.0002792 -17.41 <0.0000000000000002 ***
## coin_x 164.4245538 4.5889816 35.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000304 on 347 degrees of freedom
## Multiple R-squared: 0.7872, Adjusted R-squared: 0.7866
## F-statistic: 1284 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0038536 -0.0016410 -0.0004824 0.0009370 0.0104390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.057670 0.001762 32.72 < 0.0000000000000002 ***
## coin_x 0.182259 0.021670 8.41 0.00000000000000108 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002588 on 347 degrees of freedom
## Multiple R-squared: 0.1693, Adjusted R-squared: 0.1669
## F-statistic: 70.74 on 1 and 347 DF, p-value: 0.000000000000001076
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5779 -0.6011 -0.1128 0.4236 2.6516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.7521113 0.4882912 -19.97 <0.0000000000000002 ***
## coin_x 0.0074100 0.0001196 61.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8108 on 347 degrees of freedom
## Multiple R-squared: 0.9171, Adjusted R-squared: 0.9168
## F-statistic: 3838 on 1 and 347 DF, p-value: < 0.00000000000000022
7. Plot Time Resolution 14400
for (i in 1:10) {
coin_y <- coin_pairs_14400[["coin_y"]][i]
coin_x <- coin_pairs_14400[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[[coin_y]],
coin_x = pricing_data_14400[[coin_x]])
}## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5723 -1.8244 -0.2084 0.9656 19.3276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.71162 1.51967 23.50 <0.0000000000000002 ***
## coin_x 1.13329 0.02451 46.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.818 on 173 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9247
## F-statistic: 2138 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_FCT and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00051215 -0.00011886 -0.00000526 0.00010429 0.00068082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0004488 0.0001255 -3.577 0.000451 ***
## coin_x 0.3751717 0.0083977 44.675 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001877 on 173 degrees of freedom
## Multiple R-squared: 0.9202, Adjusted R-squared: 0.9198
## F-statistic: 1996 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.4427 -1.3155 0.0483 1.2022 8.7895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.59567 1.86460 -13.19 <0.0000000000000002 ***
## coin_x 0.81633 0.01765 46.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.241 on 173 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9247
## F-statistic: 2138 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00157619 -0.00034473 -0.00003136 0.00030762 0.00132800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0022849 0.0002835 8.06 0.00000000000012 ***
## coin_x 2.4528383 0.0549037 44.67 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00048 on 173 degrees of freedom
## Multiple R-squared: 0.9202, Adjusted R-squared: 0.9198
## F-statistic: 1996 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_DCR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0044307 -0.0014917 -0.0000537 0.0009747 0.0102198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05948 0.00182 32.685 < 0.0000000000000002 ***
## coin_x 1.62821 0.22779 7.148 0.0000000000237 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002476 on 173 degrees of freedom
## Multiple R-squared: 0.228, Adjusted R-squared: 0.2235
## F-statistic: 51.09 on 1 and 173 DF, p-value: 0.00000000002368
## [1] "Generating plots for BTC_ETH and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0038341 -0.0016368 -0.0004610 0.0009655 0.0099446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.057726 0.002486 23.22 < 0.0000000000000002 ***
## coin_x 0.181335 0.030577 5.93 0.000000016 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002568 on 173 degrees of freedom
## Multiple R-squared: 0.1689, Adjusted R-squared: 0.1641
## F-statistic: 35.17 on 1 and 173 DF, p-value: 0.00000001602
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000074526 -0.0000012903 0.0000000175 0.0000011949 0.0000057830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000033667 0.000001276 26.39 <0.0000000000000002 ***
## coin_x 0.001819995 0.000085378 21.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001909 on 173 degrees of freedom
## Multiple R-squared: 0.7243, Adjusted R-squared: 0.7227
## F-statistic: 454.4 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00061706 -0.00014454 -0.00002468 0.00009220 0.00081652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0067990 0.0002285 29.75 < 0.0000000000000002 ***
## coin_x -0.0223197 0.0028111 -7.94 0.000000000000245 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0002361 on 173 degrees of freedom
## Multiple R-squared: 0.2671, Adjusted R-squared: 0.2628
## F-statistic: 63.04 on 1 and 173 DF, p-value: 0.0000000000002455
## [1] "Generating plots for BTC_XEM and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000071121 -0.0000012184 0.0000001797 0.0000013048 0.0000033317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000358382 0.0000009897 36.21 <0.0000000000000002 ***
## coin_x 0.0048523178 0.0001916733 25.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001676 on 173 degrees of freedom
## Multiple R-squared: 0.7874, Adjusted R-squared: 0.7862
## F-statistic: 640.9 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00043173 -0.00013070 -0.00002933 0.00010251 0.00056316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0032174 0.0001248 25.77 <0.0000000000000002 ***
## coin_x 0.1194186 0.0083552 14.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001868 on 173 degrees of freedom
## Multiple R-squared: 0.5415, Adjusted R-squared: 0.5388
## F-statistic: 204.3 on 1 and 173 DF, p-value: < 0.00000000000000022
8. Plot Time Resolution 86400
for (i in 1:10) {
coin_y <- coin_pairs_86400[["coin_y"]][i]
coin_x <- coin_pairs_86400[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[[coin_y]],
coin_x = pricing_data_86400[[coin_x]])
}## [1] "Generating plots for BTC_LTC and BTC_FCT."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00063059 -0.00031038 -0.00005205 0.00020536 0.00114214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001481 0.000630 2.352 0.0262 *
## coin_x 2.625837 0.124123 21.155 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0004136 on 27 degrees of freedom
## Multiple R-squared: 0.9431, Adjusted R-squared: 0.941
## F-statistic: 447.5 on 1 and 27 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_FCT and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00037143 -0.00006396 0.00000057 0.00009761 0.00028788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0002454 0.0002513 -0.977 0.337
## coin_x 0.3591627 0.0169776 21.155 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000153 on 27 degrees of freedom
## Multiple R-squared: 0.9431, Adjusted R-squared: 0.941
## F-statistic: 447.5 on 1 and 27 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.003306 -0.001553 -0.000233 0.001127 0.004917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.058642 0.005179 11.323 0.00000000000927 ***
## coin_x 0.167030 0.063661 2.624 0.0141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002105 on 27 degrees of freedom
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.1737
## F-statistic: 6.884 on 1 and 27 DF, p-value: 0.01413
## [1] "Generating plots for BTC_ETH and BTC_DCR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0039316 -0.0011388 0.0000272 0.0010440 0.0043721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.060575 0.003816 15.874 0.00000000000000324 ***
## coin_x 1.457476 0.476382 3.059 0.00496 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002032 on 27 degrees of freedom
## Multiple R-squared: 0.2574, Adjusted R-squared: 0.2299
## F-statistic: 9.36 on 1 and 27 DF, p-value: 0.004964
## [1] "Generating plots for BTC_REP and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00028213 -0.00010269 0.00002135 0.00006380 0.00035581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0068313 0.0004061 16.824 0.000000000000000776 ***
## coin_x -0.0229002 0.0049910 -4.588 0.000092072428839062 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001651 on 27 degrees of freedom
## Multiple R-squared: 0.4381, Adjusted R-squared: 0.4173
## F-statistic: 21.05 on 1 and 27 DF, p-value: 0.00009207
## [1] "Generating plots for BTC_REP and BTC_DCR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00037576 -0.00012338 -0.00003018 0.00007637 0.00042575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0061315 0.0003475 17.643 0.000000000000000238 ***
## coin_x -0.1452727 0.0433855 -3.348 0.00241 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001851 on 27 degrees of freedom
## Multiple R-squared: 0.2934, Adjusted R-squared: 0.2672
## F-statistic: 11.21 on 1 and 27 DF, p-value: 0.002406
## [1] "Generating plots for BTC_DCR and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0010470 -0.0002754 0.0001094 0.0003866 0.0006420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0004812 0.0011910 -0.404 0.689
## coin_x 0.1041891 0.0146396 7.117 0.000000119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0004842 on 27 degrees of freedom
## Multiple R-squared: 0.6523, Adjusted R-squared: 0.6394
## F-statistic: 50.65 on 1 and 27 DF, p-value: 0.0000001185
## [1] "Generating plots for BTC_ETH and BTC_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0032331 -0.0018057 -0.0002109 0.0012201 0.0046517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075642 0.007091 10.667 0.0000000000349 ***
## coin_x -0.135217 0.277424 -0.487 0.63
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002348 on 27 degrees of freedom
## Multiple R-squared: 0.008722, Adjusted R-squared: -0.02799
## F-statistic: 0.2376 on 1 and 27 DF, p-value: 0.6299
## [1] "Generating plots for BTC_DASH and BTC_DCR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0056744 -0.0029498 -0.0004756 0.0029110 0.0100101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.031220 0.007047 4.431 0.000141 ***
## coin_x 6.260641 0.879679 7.117 0.000000119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003753 on 27 degrees of freedom
## Multiple R-squared: 0.6523, Adjusted R-squared: 0.6394
## F-statistic: 50.65 on 1 and 27 DF, p-value: 0.0000001185
## [1] "Generating plots for BTC_LSK and BTC_ZEC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00020605 -0.00006745 0.00002737 0.00004922 0.00013792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0018896 0.0001345 14.051 0.0000000000000619 ***
## coin_x -0.0065288 0.0024493 -2.666 0.0128 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00008788 on 27 degrees of freedom
## Multiple R-squared: 0.2083, Adjusted R-squared: 0.179
## F-statistic: 7.105 on 1 and 27 DF, p-value: 0.01282
9. BTC_XEM and BTC_LTC Coin Pair
An examination of BTC_XEM and BTC_LTC across time resolutions.
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[["BTC_XEM"]],
coin_x = pricing_data_300[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001342 0.000000035 0.000001233 0.000006823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000342914 0.0000001786 192.0 <0.0000000000000002 ***
## coin_x 0.0017825242 0.0000119346 149.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001836 on 8351 degrees of freedom
## Multiple R-squared: 0.7276, Adjusted R-squared: 0.7276
## F-statistic: 2.231e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[["BTC_XEM"]],
coin_x = pricing_data_900[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001345 0.000000029 0.000001240 0.000006653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000034272 0.000000309 110.92 <0.0000000000000002 ***
## coin_x 0.001783861 0.000020643 86.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001835 on 2783 degrees of freedom
## Multiple R-squared: 0.7285, Adjusted R-squared: 0.7284
## F-statistic: 7467 on 1 and 2783 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[["BTC_XEM"]],
coin_x = pricing_data_1800[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075557 -0.0000013567 -0.0000000041 0.0000012434 0.0000065578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000343156 0.0000004359 78.73 <0.0000000000000002 ***
## coin_x 0.0017808785 0.0000291278 61.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001833 on 1391 degrees of freedom
## Multiple R-squared: 0.7288, Adjusted R-squared: 0.7286
## F-statistic: 3738 on 1 and 1391 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[["BTC_XEM"]],
coin_x = pricing_data_7200[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075117 -0.0000013231 -0.0000000155 0.0000012043 0.0000060980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000341619 0.0000008857 38.57 <0.0000000000000002 ***
## coin_x 0.0017887492 0.0000592241 30.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001867 on 347 degrees of freedom
## Multiple R-squared: 0.7244, Adjusted R-squared: 0.7236
## F-statistic: 912.2 on 1 and 347 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[["BTC_XEM"]],
coin_x = pricing_data_14400[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000074526 -0.0000012903 0.0000000175 0.0000011949 0.0000057830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000033667 0.000001276 26.39 <0.0000000000000002 ***
## coin_x 0.001819995 0.000085378 21.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001909 on 173 degrees of freedom
## Multiple R-squared: 0.7243, Adjusted R-squared: 0.7227
## F-statistic: 454.4 on 1 and 173 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[["BTC_XEM"]],
coin_x = pricing_data_86400[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000072622 -0.0000010911 0.0000001578 0.0000013416 0.0000027461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000034025 0.000003437 9.899 0.000000000176 ***
## coin_x 0.001780634 0.000232186 7.669 0.000000030065 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000002092 on 27 degrees of freedom
## Multiple R-squared: 0.6854, Adjusted R-squared: 0.6737
## F-statistic: 58.81 on 1 and 27 DF, p-value: 0.00000003007
9. USDT_REP and USDT_BTC Coin Pair
An examination of USDT_REP and USDT_BTC across time resolutions.
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[["USDT_REP"]],
coin_x = pricing_data_300[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8470 -0.5730 -0.1290 0.4191 4.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76564090 0.10152549 -96.19 <0.0000000000000002 ***
## coin_x 0.00741490 0.00002486 298.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8201 on 8351 degrees of freedom
## Multiple R-squared: 0.9142, Adjusted R-squared: 0.9142
## F-statistic: 8.894e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[["USDT_REP"]],
coin_x = pricing_data_900[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7288 -0.5774 -0.1290 0.4189 4.2573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.73062649 0.17668477 -55.07 <0.0000000000000002 ***
## coin_x 0.00740622 0.00004327 171.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 2783 degrees of freedom
## Multiple R-squared: 0.9132, Adjusted R-squared: 0.9132
## F-statistic: 2.929e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[["USDT_REP"]],
coin_x = pricing_data_1800[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7184 -0.5726 -0.1294 0.4224 4.2610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76197830 0.24983070 -39.07 <0.0000000000000002 ***
## coin_x 0.00741325 0.00006119 121.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 1391 degrees of freedom
## Multiple R-squared: 0.9134, Adjusted R-squared: 0.9134
## F-statistic: 1.468e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[["USDT_REP"]],
coin_x = pricing_data_7200[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5779 -0.6011 -0.1128 0.4236 2.6516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.7521113 0.4882912 -19.97 <0.0000000000000002 ***
## coin_x 0.0074100 0.0001196 61.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8108 on 347 degrees of freedom
## Multiple R-squared: 0.9171, Adjusted R-squared: 0.9168
## F-statistic: 3838 on 1 and 347 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[["USDT_REP"]],
coin_x = pricing_data_14400[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55987 -0.59252 -0.09366 0.44002 2.14749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.6484195 0.6844643 -14.10 <0.0000000000000002 ***
## coin_x 0.0073797 0.0001676 44.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8098 on 173 degrees of freedom
## Multiple R-squared: 0.9181, Adjusted R-squared: 0.9176
## F-statistic: 1939 on 1 and 173 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[["USDT_REP"]],
coin_x = pricing_data_86400[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4944 -0.3292 -0.1622 0.4061 1.5361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.9069160 1.4791046 -4.67 0.000074 ***
## coin_x 0.0066972 0.0003638 18.41 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6775 on 27 degrees of freedom
## Multiple R-squared: 0.9262, Adjusted R-squared: 0.9235
## F-statistic: 338.9 on 1 and 27 DF, p-value: < 0.00000000000000022
10. USDT_XMR and USDT_LTC Coin Pair
An examination of USDT_XMR and USDT_LTC across time resolutions.
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[["USDT_XMR"]],
coin_x = pricing_data_300[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.7857 -1.6548 -0.3121 1.0235 24.4679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.96177 0.23045 156.1 <0.0000000000000002 ***
## coin_x 1.13133 0.00371 304.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.976 on 8351 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9176
## F-statistic: 9.297e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[["USDT_XMR"]],
coin_x = pricing_data_900[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7046 -1.6898 -0.3227 1.0009 24.1214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.967359 0.398511 90.25 <0.0000000000000002 ***
## coin_x 1.131332 0.006417 176.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.971 on 2783 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.9178
## F-statistic: 3.108e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[["USDT_XMR"]],
coin_x = pricing_data_1800[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6872 -1.6639 -0.3182 0.9912 24.1493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.992859 0.560945 64.17 <0.0000000000000002 ***
## coin_x 1.130652 0.009035 125.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.957 on 1391 degrees of freedom
## Multiple R-squared: 0.9184, Adjusted R-squared: 0.9184
## F-statistic: 1.566e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[["USDT_XMR"]],
coin_x = pricing_data_7200[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6386 -1.6524 -0.2642 0.9648 19.3184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.99738 1.08940 33.04 <0.0000000000000002 ***
## coin_x 1.12981 0.01756 64.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.855 on 347 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9224
## F-statistic: 4140 on 1 and 347 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[["USDT_XMR"]],
coin_x = pricing_data_14400[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5723 -1.8244 -0.2084 0.9656 19.3276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.71162 1.51967 23.50 <0.0000000000000002 ***
## coin_x 1.13329 0.02451 46.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.818 on 173 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9247
## F-statistic: 2138 on 1 and 173 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[["USDT_XMR"]],
coin_x = pricing_data_86400[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.410 -1.666 0.186 1.522 5.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.92702 2.45102 16.29 0.00000000000000172 ***
## coin_x 1.05803 0.04013 26.36 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.415 on 27 degrees of freedom
## Multiple R-squared: 0.9626, Adjusted R-squared: 0.9612
## F-statistic: 695.1 on 1 and 27 DF, p-value: < 0.00000000000000022
